diff --git a/.ipynb_checkpoints/clean_combined_customer_data-checkpoint.csv b/.ipynb_checkpoints/clean_combined_customer_data-checkpoint.csv
new file mode 100644
index 0000000..4f95dd5
--- /dev/null
+++ b/.ipynb_checkpoints/clean_combined_customer_data-checkpoint.csv
@@ -0,0 +1,9136 @@
+customer_id,state,gender,education,customer_lifetime_value,income,monthly_premium_auto,number_of_open_complaints,policy_type,vehicle_class,total_claim_amount
+rb50392,washington,unknown,master,0.0,0.0,1000.0,0,personal auto,four-door car,2.704934
+qz44356,arizona,f,bachelor,697953.59,0.0,94.0,0,personal auto,four-door car,1131.464935
+ai49188,nevada,f,bachelor,1288743.17,48767.0,108.0,0,personal auto,two-door car,566.472247
+ww63253,california,m,bachelor,764586.18,0.0,106.0,0,corporate auto,suv,529.881344
+ga49547,washington,m,high school or below,536307.65,36357.0,68.0,0,personal auto,four-door car,17.269323
+oc83172,oregon,f,bachelor,825629.78,62902.0,69.0,0,personal auto,two-door car,159.383042
+xz87318,oregon,f,college,538089.86,55350.0,67.0,0,corporate auto,four-door car,321.6
+cf85061,arizona,m,master,721610.03,0.0,101.0,0,corporate auto,four-door car,363.02968
+dy87989,oregon,m,bachelor,2412750.4,14072.0,71.0,0,corporate auto,four-door car,511.2
+bq94931,oregon,f,college,738817.81,28812.0,93.0,0,special auto,four-door car,425.527834
+sx51350,california,m,college,473899.2,0.0,67.0,0,personal auto,four-door car,482.4
+vq65197,california,unknown,college,819719.71,0.0,110.0,0,personal auto,suv,528.0
+dp39365,california,unknown,master,879879.7,77026.0,110.0,2,corporate auto,four-door car,472.029737
+sj95423,arizona,unknown,high school or below,881901.89,99845.0,110.0,1,corporate auto,suv,528.0
+il66569,california,unknown,college,538443.17,83689.0,70.0,2,corporate auto,four-door car,307.139132
+bw63560,oregon,unknown,bachelor,746313.94,24599.0,64.0,1,corporate auto,four-door car,42.920271
+fv94802,nevada,unknown,high school or below,256686.78,25049.0,67.0,0,personal auto,two-door car,454.245098
+oe15005,california,unknown,college,394524.16,28855.0,101.0,0,personal auto,suv,647.442031
+wc83389,oregon,unknown,college,571033.31,51148.0,72.0,0,personal auto,four-door car,308.981664
+fl50705,california,unknown,high school or below,816261.71,66140.0,101.0,0,corporate auto,four-door car,484.8
+zk25313,oregon,unknown,high school or below,287205.13,57749.0,74.0,0,personal auto,two-door car,355.2
+qk46697,washington,m,bachelor,617710.93,61040.0,79.0,1,personal auto,two-door car,20.382876
+yh23384,arizona,unknown,bachelor,2412750.4,14072.0,71.0,0,personal auto,four-door car,511.2
+tz98966,nevada,unknown,bachelor,245019.1,0.0,73.0,3,corporate auto,four-door car,554.376763
+hm55802,california,unknown,bachelor,239210.79,17870.0,61.0,0,corporate auto,four-door car,439.2
+fs42516,oregon,unknown,college,580206.6,97541.0,72.0,0,personal auto,four-door car,389.185006
+us89481,california,unknown,bachelor,394637.21,0.0,111.0,0,personal auto,four-door car,799.2
+hs14476,washington,m,college,916206.32,29723.0,80.0,0,personal auto,four-door car,20.985105
+ge62437,arizona,unknown,college,1290256.01,86584.0,111.0,2,personal auto,four-door car,532.8
+ej77678,oregon,unknown,master,323536.05,75690.0,80.0,1,personal auto,four-door car,384.0
+sv85652,arizona,unknown,college,245458.35,23158.0,63.0,1,personal auto,four-door car,322.294043
+ul64533,nevada,unknown,high school or below,1897545.61,65999.0,237.0,0,corporate auto,luxury,615.927769
+pf41800,california,unknown,bachelor,471532.13,0.0,65.0,0,personal auto,four-door car,308.15089
+hd95276,washington,f,college,473787.17,0.0,130.0,0,personal auto,suv,23.820158
+sk67821,oregon,unknown,bachelor,493291.63,37260.0,62.0,0,corporate auto,four-door car,15.437681
+yv55495,arizona,unknown,high school or below,574422.97,68987.0,71.0,0,personal auto,four-door car,204.475147
+ky38074,california,unknown,bachelor,1389173.57,42305.0,117.0,0,personal auto,four-door car,561.6
+dm79012,oregon,unknown,master,738097.67,65706.0,91.0,0,personal auto,four-door car,436.8
+cm61827,oregon,unknown,bachelor,309003.41,0.0,90.0,0,personal auto,two-door car,648.0
+wc35801,arizona,unknown,high school or below,252163.31,53243.0,66.0,2,personal auto,four-door car,157.397849
+qg25316,nevada,unknown,high school or below,265206.18,0.0,70.0,1,corporate auto,two-door car,484.318536
+mb98372,oregon,unknown,college,277104.5,50071.0,71.0,0,corporate auto,two-door car,18.918935
+il19217,california,unknown,bachelor,393900.64,60021.0,99.0,0,personal auto,four-door car,882.871945
+sr38658,arizona,unknown,high school or below,1223187.97,43244.0,103.0,0,personal auto,luxury,494.4
+yd87931,washington,m,doctor,495165.61,46896.0,35354.0,1,personal auto,four-door car,31.707317
+hg65722,oregon,unknown,doctor,1281910.29,10105.0,172.0,3,personal auto,suv,0.517753
+bu27331,arizona,unknown,bachelor,446851.05,0.0,73.0,3,personal auto,four-door car,579.165954
+xm45289,oregon,unknown,high school or below,551434.4,23218.0,71.0,0,personal auto,two-door car,447.79344
+kp34198,california,unknown,bachelor,334387.53,0.0,92.0,0,corporate auto,four-door car,529.624084
+sh90947,arizona,unknown,high school or below,229447.89,0.0,62.0,0,personal auto,four-door car,313.023175
+we95729,oregon,unknown,college,3670742.64,24804.0,104.0,0,personal auto,suv,593.830288
+py51963,california,unknown,bachelor,3347334.95,33190.0,106.0,0,corporate auto,suv,508.8
+rb69909,nevada,unknown,high school or below,798343.17,36014.0,69.0,3,special auto,four-door car,173.956072
+nw21079,washington,f,master,487938.48,67163.0,61.0,2,personal auto,two-door car,33.192803
+fr46645,california,unknown,bachelor,429399.73,16701.0,113.0,0,personal auto,four-door car,831.625979
+sy17488,arizona,unknown,college,716439.55,46623.0,91.0,0,corporate auto,four-door car,436.8
+ap67935,california,unknown,college,761951.58,64749.0,64.0,0,special auto,four-door car,302.56519
+fs37417,arizona,unknown,high school or below,395800.28,0.0,101.0,0,personal auto,suv,484.8
+ml29312,oregon,unknown,high school or below,449949.33,16969.0,124.0,2,personal auto,suv,704.768111
+ub61619,oregon,unknown,master,405956.74,11621.0,108.0,0,personal auto,four-door car,518.4
+cd86811,arizona,unknown,bachelor,445811.34,17622.0,65.0,1,personal auto,four-door car,312.0
+ru83859,california,unknown,bachelor,811033.31,11489.0,105.0,0,personal auto,two-door car,504.0
+fg63582,oregon,unknown,bachelor,333976.49,0.0,94.0,0,personal auto,two-door car,863.327324
+nn71951,california,unknown,high school or below,2426101.78,66525.0,100.0,0,personal auto,suv,104.331355
+wb37082,arizona,unknown,bachelor,661397.37,0.0,63.0,0,personal auto,four-door car,676.391482
+sm52139,washington,unknown,college,293069.35,33663.0,73.0,0,personal auto,four-door car,350.4
+fl82372,oregon,unknown,college,867219.43,22547.0,112.0,0,corporate auto,suv,537.6
+dp45816,arizona,unknown,high school or below,1163866.93,61486.0,97.0,0,personal auto,two-door car,465.258644
+gw33762,oregon,unknown,bachelor,684615.03,0.0,95.0,0,personal auto,two-door car,456.0
+rz33670,california,unknown,college,1172777.65,29879.0,102.0,0,personal auto,four-door car,500.254235
+py70169,oregon,unknown,high school or below,2264383.48,93011.0,113.0,0,personal auto,suv,281.451042
+mo91628,oregon,unknown,master,261447.43,65186.0,65.0,0,personal auto,two-door car,5.434505
+hw87852,oregon,unknown,master,245175.27,26840.0,64.0,2,personal auto,four-door car,307.2
+hb20453,oregon,unknown,bachelor,678127.02,0.0,104.0,1,personal auto,luxury,982.399613
+bn87372,oregon,unknown,bachelor,497480.15,75644.0,65.0,3,personal auto,two-door car,467.803638
+yx23800,oregon,unknown,bachelor,859160.49,38984.0,73.0,0,personal auto,four-door car,350.4
+dz87709,oregon,unknown,high school or below,559216.14,71811.0,71.0,0,personal auto,four-door car,29.03416
+xw13033,nevada,unknown,college,800947.28,20961.0,67.0,0,personal auto,four-door car,321.6
+sp81997,washington,f,master,0.0,41275.0,96.0,0,personal auto,four-door car,41.122303
+om82309,california,unknown,bachelor,5816655.35,61321.0,186.0,1,personal auto,luxury,427.63121
+zu35962,california,unknown,college,802522.94,0.0,77.0,0,personal auto,two-door car,25.807685
+vh85817,california,unknown,high school or below,578018.22,51066.0,74.0,0,personal auto,four-door car,787.993313
+dt85712,california,unknown,doctor,411853.91,34378.0,102.0,0,personal auto,suv,489.6
+yj88573,nevada,unknown,master,252307.02,43072.0,63.0,0,personal auto,four-door car,302.4
+sq19467,oregon,unknown,college,655421.64,25222.0,90.0,0,personal auto,four-door car,475.623251
+yb66933,washington,f,bachelor,538275.2,77552.0,68.0,1,corporate auto,four-door car,45.215059
+et79815,california,unknown,college,592672.94,23091.0,96.0,5,personal auto,four-door car,460.8
+qc35222,california,unknown,bachelor,268347.07,48269.0,69.0,3,corporate auto,four-door car,282.151207
+cj15590,oregon,unknown,bachelor,269518.24,32720.0,67.0,0,personal auto,four-door car,321.6
+oi48267,california,unknown,college,604702.52,20396.0,76.0,1,personal auto,four-door car,364.8
+jy67916,oregon,unknown,college,1317101.28,21513.0,119.0,1,personal auto,two-door car,679.827592
+ow15518,washington,f,college,1595001.95,0.0,87.0,1,personal auto,two-door car,46.041452
+cz33664,oregon,unknown,bachelor,252765.38,80744.0,63.0,0,corporate auto,four-door car,11.879037
+wk30175,oregon,unknown,bachelor,267209.58,52822.0,67.0,0,corporate auto,two-door car,350.529033
+on44465,california,unknown,high school or below,531329.4,0.0,77.0,1,personal auto,four-door car,863.3947
+tv87155,oregon,unknown,bachelor,2094619.25,69738.0,74.0,1,personal auto,four-door car,492.127532
+kh48895,arizona,unknown,master,837535.39,17780.0,109.0,0,personal auto,suv,132.588288
+nz30757,california,unknown,bachelor,480166.15,18107.0,62.0,0,personal auto,four-door car,297.6
+ri22468,arizona,unknown,bachelor,574594.33,57740.0,74.0,3,personal auto,four-door car,269.905129
+fz30935,oregon,unknown,college,606611.6,32627.0,76.0,0,personal auto,two-door car,380.036697
+ug93476,california,unknown,college,800230.83,0.0,107.0,0,personal auto,suv,513.6
+ab96670,california,unknown,college,239391.54,0.0,70.0,0,personal auto,four-door car,425.266308
+xk64261,oregon,unknown,bachelor,476281.79,65795.0,62.0,1,corporate auto,two-door car,49.011099
+ev68375,california,unknown,college,433038.6,60475.0,107.0,0,personal auto,four-door car,513.6
+un51653,california,unknown,high school or below,940272.98,0.0,130.0,0,personal auto,suv,936.0
+to96662,oregon,unknown,bachelor,696669.45,41837.0,88.0,0,personal auto,four-door car,142.062768
+mr52087,california,unknown,bachelor,769406.43,32303.0,65.0,0,personal auto,four-door car,45.152521
+de75225,oregon,unknown,high school or below,871756.11,0.0,117.0,1,personal auto,suv,561.6
+ep80820,california,unknown,bachelor,592874.85,40531.0,74.0,0,personal auto,two-door car,30.567357
+gu99037,oregon,unknown,college,245297.73,79898.0,62.0,1,corporate auto,four-door car,271.606799
+wi57118,california,unknown,college,670157.17,56398.0,85.0,0,personal auto,four-door car,408.0
+gp39118,washington,m,high school or below,499655.27,71600.0,63.0,0,personal auto,two-door car,46.158117
+mq14219,arizona,unknown,college,849269.88,27804.0,109.0,0,personal auto,suv,784.8
+kn20603,oregon,unknown,bachelor,771349.4,45506.0,66.0,2,personal auto,four-door car,316.8
+sg20925,washington,f,master,518579.76,99428.0,6464.0,1,personal auto,four-door car,48.046869
+ji70886,california,unknown,high school or below,729006.98,0.0,102.0,1,corporate auto,suv,489.6
+vf72557,oregon,unknown,high school or below,477294.38,20993.0,133.0,0,personal auto,suv,638.4
+ox28638,arizona,unknown,high school or below,680649.14,37839.0,86.0,0,personal auto,two-door car,465.633954
+sk55033,california,unknown,bachelor,246978.13,92711.0,62.0,0,personal auto,two-door car,368.400146
+oo88645,california,unknown,college,310392.3,74665.0,78.0,2,corporate auto,four-door car,236.902001
+fm14335,washington,f,college,1048491.54,61108.0,89.0,0,personal auto,four-door car,49.451117
+cw82151,california,unknown,bachelor,890273.76,46833.0,112.0,0,personal auto,luxury,64.109663
+dy22043,oregon,unknown,high school or below,549944.72,88768.0,68.0,0,personal auto,two-door car,326.4
+sh36774,california,unknown,high school or below,1502359.86,28262.0,192.0,0,personal auto,luxury,921.6
+em43724,nevada,unknown,bachelor,250910.79,33555.0,63.0,0,personal auto,four-door car,101.89645
+fh85960,oregon,unknown,college,3122174.81,42780.0,113.0,1,personal auto,four-door car,542.4
+xa55993,oregon,unknown,master,313350.34,58850.0,78.0,0,personal auto,four-door car,143.747794
+go36627,arizona,unknown,college,383745.19,21880.0,97.0,0,special auto,four-door car,424.077159
+qh48047,oregon,unknown,college,1179049.62,25251.0,66.0,0,personal auto,four-door car,316.8
+hs28694,washington,f,high school or below,282986.39,25317.0,71.0,0,personal auto,two-door car,50.422181
+uh65059,arizona,unknown,high school or below,430580.83,24188.0,109.0,0,personal auto,luxury,523.2
+fv21968,nevada,unknown,bachelor,597397.69,41611.0,74.0,0,special auto,four-door car,113.801497
+qa87025,oregon,unknown,bachelor,567044.24,28406.0,72.0,1,personal auto,four-door car,192.87572
+tq13499,arizona,unknown,college,473174.93,69833.0,118.0,0,personal auto,four-door car,828.662719
+bu44523,arizona,unknown,high school or below,802637.96,0.0,74.0,0,corporate auto,four-door car,532.8
+ms41162,arizona,unknown,high school or below,402296.35,0.0,117.0,0,personal auto,suv,975.107098
+pu25891,oregon,unknown,high school or below,961831.09,80536.0,119.0,0,special auto,luxury,53.798708
+lh92841,washington,f,bachelor,725595.38,88891.0,89.0,0,personal auto,four-door car,50.528355
+sx26335,oregon,unknown,college,955292.69,97732.0,79.0,0,personal auto,four-door car,289.9122
+az95587,arizona,f,bachelor,1038855.32,61222.0,89.0,0,special auto,four-door car,392.604371
+ds81757,oregon,m,college,247012.12,0.0,74.0,0,personal auto,two-door car,721.242206
+oj94107,arizona,m,master,561906.85,50335.0,140.0,0,personal auto,suv,456.52385
+lp84436,california,f,high school or below,904711.92,0.0,127.0,0,personal auto,luxury,1087.995426
+ff22360,washington,f,doctor,268731.41,82210.0,68.0,4,personal auto,four-door car,51.961915
+lm19287,oregon,f,high school or below,373150.46,0.0,96.0,0,personal auto,four-door car,460.8
+zu18643,nevada,m,bachelor,366077.03,64495.0,92.0,0,corporate auto,four-door car,251.992083
+az82578,california,f,college,792882.93,0.0,72.0,0,corporate auto,four-door car,345.6
+xc67861,arizona,f,bachelor,501175.16,28859.0,67.0,3,corporate auto,two-door car,321.6
+yc43143,arizona,f,high school or below,798825.83,77330.0,99.0,0,personal auto,four-door car,99.257608
+ek59571,oregon,m,college,388545.64,0.0,105.0,0,corporate auto,four-door car,504.0
+pa38372,washington,f,high school or below,935773.78,33060.0,117.0,0,personal auto,four-door car,56.731578
+ro18530,washington,m,master,254068.98,42557.0,65.0,0,personal auto,four-door car,57.562324
+pd27940,arizona,m,high school or below,488516.25,26372.0,126.0,0,personal auto,suv,604.8
+bs77946,arizona,f,bachelor,975330.71,17514.0,127.0,0,personal auto,suv,8.312729
+ym50253,california,f,bachelor,294615.37,89270.0,74.0,0,personal auto,four-door car,316.599228
+nr15332,california,m,high school or below,258111.09,29757.0,65.0,0,personal auto,four-door car,312.0
+rc62865,oregon,m,college,351738.58,51814.0,92.0,0,personal auto,four-door car,56.300724
+cc15295,oregon,f,bachelor,974335.01,24028.0,82.0,0,personal auto,four-door car,393.6
+ka61892,arizona,m,college,387364.7,28142.0,105.0,0,personal auto,luxury,701.708239
+os94884,arizona,f,bachelor,764928.2,52705.0,64.0,1,personal auto,two-door car,128.705563
+nd87334,california,m,high school or below,228759.69,0.0,63.0,0,corporate auto,two-door car,679.368378
+oy51402,washington,f,high school or below,825576.39,54040.0,103.0,0,personal auto,suv,59.987126
+yl74911,california,f,college,871492.21,0.0,118.0,0,personal auto,suv,566.4
+gk92563,washington,f,doctor,681923.12,22492.0,85.0,0,personal auto,two-door car,61.654262
+hl53154,california,m,bachelor,741619.73,0.0,77.0,2,personal auto,four-door car,554.4
+ri78966,oregon,m,master,777115.9,21876.0,68.0,0,personal auto,four-door car,465.41477
+ic13702,california,f,bachelor,696834.19,0.0,69.0,1,personal auto,four-door car,496.8
+be10809,california,f,high school or below,425028.26,0.0,61.0,1,personal auto,four-door car,292.8
+ht87217,oregon,f,high school or below,1977656.65,70699.0,82.0,0,personal auto,four-door car,256.813837
+th95618,california,f,high school or below,2134346.6,0.0,74.0,1,personal auto,four-door car,355.2
+ts19868,california,m,college,241313.97,27501.0,63.0,0,personal auto,four-door car,542.319401
+lp45550,oregon,f,doctor,1536384.72,15897.0,101.0,0,personal auto,four-door car,303.148399
+qr87004,nevada,f,college,845696.19,25141.0,73.0,1,personal auto,four-door car,25.438063
+oe75747,nevada,f,college,218964.25,0.0,61.0,3,personal auto,four-door car,292.8
+dx91392,california,m,high school or below,578018.22,51066.0,74.0,0,personal auto,four-door car,787.993313
+ab72731,california,f,bachelor,463998.16,28358.0,117.0,0,personal auto,luxury,84.024413
+gx84338,washington,f,doctor,382443.13,62530.0,95.0,0,personal auto,four-door car,61.693771
+is12901,arizona,f,bachelor,596811.89,90972.0,74.0,0,personal auto,two-door car,232.926145
+bn90616,washington,f,bachelor,859033.5,63110.0,72.0,0,personal auto,four-door car,68.179721
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+ab69140,california,m,master,25093.57037,36007.0,70.0,0,personal auto,four-door car,336.0
+ct53265,california,f,college,2656.846648,18834.0,69.0,0,personal auto,two-door car,453.895633
+wn91223,california,m,master,11073.10572,82807.0,92.0,0,personal auto,four-door car,302.458432
+mp48946,california,f,high school or below,2499.469914,72021.0,62.0,0,corporate auto,two-door car,50.740536
+ne79174,california,m,bachelor,5386.512585,41228.0,70.0,3,personal auto,two-door car,336.0
+ui77960,california,f,high school or below,6656.200678,39834.0,83.0,0,personal auto,four-door car,398.4
+vd69291,california,m,college,2702.692335,22415.0,69.0,0,personal auto,four-door car,352.894843
+xd54132,california,f,high school or below,2567.346739,61900.0,64.0,0,corporate auto,four-door car,307.2
+at33529,california,f,doctor,2776.748342,30893.0,70.0,0,corporate auto,four-door car,85.416096
+at79008,california,f,bachelor,9065.25926,40006.0,114.0,1,personal auto,two-door car,132.568435
+uz14076,california,f,high school or below,32104.69957,89546.0,266.0,1,corporate auto,luxury,2452.894264
+ep10645,california,m,college,18677.85248,0.0,86.0,0,personal auto,four-door car,538.649619
+pw78930,california,m,bachelor,2982.196702,79523.0,74.0,0,personal auto,four-door car,132.023007
+zc97968,california,m,college,2736.352663,17533.0,73.0,0,personal auto,two-door car,350.4
+sf30642,california,m,high school or below,8996.774075,49706.0,113.0,0,personal auto,suv,617.515196
+ah22694,california,m,doctor,5850.451386,24317.0,73.0,1,corporate auto,four-door car,350.4
+sm87252,california,m,college,5843.614767,77498.0,74.0,0,personal auto,two-door car,249.151199
+ma60552,california,m,bachelor,4015.400868,28088.0,102.0,0,personal auto,suv,489.6
+pe51299,california,m,high school or below,10781.61334,73026.0,67.0,0,personal auto,four-door car,321.6
+jr22900,california,f,college,18484.80858,36153.0,117.0,1,corporate auto,luxury,323.424189
+dp19210,california,f,master,9191.733549,23823.0,115.0,0,corporate auto,luxury,567.48055
+xp76338,california,m,bachelor,4711.038869,24843.0,61.0,2,personal auto,four-door car,331.325638
+mw46400,california,f,high school or below,7634.855287,0.0,106.0,0,corporate auto,four-door car,763.2
+fb71914,california,m,college,30205.01297,0.0,89.0,0,personal auto,four-door car,469.359715
+oh15780,california,m,college,12280.76661,88340.0,102.0,0,personal auto,two-door car,489.6
+qo56615,california,f,college,2980.010499,13535.0,80.0,1,personal auto,four-door car,384.0
+el67979,california,m,college,17223.06591,22346.0,64.0,0,personal auto,four-door car,307.2
+ow68478,california,m,high school or below,12298.6863,17101.0,113.0,0,corporate auto,suv,542.4
+sf13677,california,m,high school or below,2843.149061,0.0,81.0,0,personal auto,four-door car,583.2
+mp80287,california,f,college,9498.473472,0.0,90.0,0,personal auto,two-door car,432.0
+uk45178,california,f,bachelor,2224.555883,0.0,67.0,1,personal auto,two-door car,482.4
+jc29850,california,m,bachelor,7506.964816,0.0,67.0,1,corporate auto,two-door car,321.6
+ik44574,california,m,bachelor,5608.819189,28483.0,72.0,0,corporate auto,four-door car,345.6
+ke98143,california,f,master,2700.676449,89362.0,67.0,0,personal auto,four-door car,116.209568
+xn28388,california,f,college,4130.382954,0.0,66.0,2,corporate auto,four-door car,523.030597
+ax38728,california,m,college,2966.944264,0.0,81.0,0,personal auto,two-door car,388.8
+ix18485,california,f,high school or below,9594.248898,27443.0,86.0,2,personal auto,four-door car,685.048914
+kb42322,california,f,bachelor,6761.107268,70829.0,85.0,0,personal auto,four-door car,553.174109
+rw54189,california,m,bachelor,5747.268347,23504.0,73.0,0,personal auto,four-door car,350.4
+fv70604,california,f,bachelor,5048.512037,0.0,142.0,0,corporate auto,suv,880.435943
+pc23709,california,f,bachelor,5714.562292,74905.0,72.0,0,personal auto,four-door car,167.028453
+rb75458,california,m,high school or below,2230.275142,0.0,65.0,0,personal auto,four-door car,712.71287
+rx14132,california,m,high school or below,7228.005386,25926.0,61.0,0,personal auto,two-door car,292.8
+af36894,california,m,bachelor,4392.982538,33640.0,113.0,0,corporate auto,suv,542.4
+uy32623,california,f,college,3593.12645,18213.0,96.0,1,corporate auto,four-door car,460.8
+al95338,california,f,high school or below,6679.139606,0.0,91.0,0,personal auto,four-door car,655.2
+wl28610,california,f,high school or below,4146.409412,0.0,116.0,1,corporate auto,suv,556.8
+ac73173,california,m,bachelor,3694.647081,0.0,102.0,0,personal auto,suv,610.617285
+gv76798,california,f,master,4781.821034,30361.0,119.0,1,personal auto,four-door car,373.289571
+sp29219,california,m,college,3885.456401,0.0,105.0,0,personal auto,four-door car,504.0
+ci30899,california,f,college,4737.411455,0.0,68.0,0,personal auto,two-door car,326.4
+mi49191,california,m,bachelor,9410.670129,96060.0,117.0,0,corporate auto,four-door car,561.6
+yp16210,california,f,college,5878.447428,25398.0,74.0,0,personal auto,four-door car,465.922152
+co91673,california,f,bachelor,4547.321823,29031.0,61.0,2,personal auto,four-door car,292.8
+cc93587,california,m,college,5926.38544,92949.0,74.0,0,personal auto,two-door car,337.305815
+mx48796,california,f,bachelor,4873.672682,0.0,68.0,0,corporate auto,four-door car,356.08323
+ic80537,california,m,college,2580.849899,46900.0,66.0,0,corporate auto,four-door car,316.8
+aa93585,california,m,college,7083.642205,97024.0,177.0,0,personal auto,suv,686.815162
+sr41132,california,f,high school or below,30104.51969,0.0,94.0,0,corporate auto,two-door car,848.737364
+it11766,california,f,bachelor,4148.570285,61896.0,104.0,0,personal auto,suv,461.306722
+qo68842,california,f,doctor,2518.743544,39317.0,64.0,0,personal auto,four-door car,77.695607
+ry47606,california,m,bachelor,3843.965188,43987.0,96.0,0,personal auto,four-door car,460.8
+cx22458,california,m,master,9075.768214,37722.0,116.0,0,personal auto,luxury,158.077504
+tl44525,california,m,bachelor,2619.337376,78618.0,66.0,0,corporate auto,two-door car,225.578841
+cz57208,california,f,bachelor,15245.25495,30205.0,195.0,0,personal auto,luxury,1329.957905
+lm97847,california,m,college,2615.13922,57023.0,67.0,0,personal auto,four-door car,250.652309
+ik96366,california,m,college,5551.398167,36918.0,76.0,3,personal auto,two-door car,364.8
+sj86522,california,m,high school or below,6827.044423,0.0,102.0,0,corporate auto,suv,956.689634
+uu42868,california,m,high school or below,5619.689084,0.0,153.0,0,corporate auto,suv,1027.000029
+gx11087,california,f,high school or below,3907.028747,0.0,109.0,0,personal auto,suv,666.042098
+jr41742,california,m,college,3622.872124,0.0,108.0,0,corporate auto,suv,1254.177129
+ra89822,california,f,high school or below,34611.37896,20090.0,109.0,0,personal auto,luxury,523.2
+ij25462,california,m,bachelor,2845.520933,86631.0,73.0,1,personal auto,four-door car,400.832857
+zp86347,california,f,college,5500.577411,44019.0,138.0,0,personal auto,suv,165.478147
+ho82823,california,f,bachelor,3358.532935,59367.0,84.0,0,personal auto,four-door car,6.880385
+ea54906,california,f,high school or below,7501.661322,38874.0,94.0,0,corporate auto,four-door car,331.170924
+bc64697,california,f,college,11663.98572,0.0,107.0,0,personal auto,four-door car,513.6
+vv68726,california,f,high school or below,5133.397765,28647.0,69.0,0,personal auto,four-door car,331.2
+qr91631,california,f,high school or below,8732.090534,51205.0,72.0,0,personal auto,two-door car,185.530076
+ot56964,california,m,master,9424.256842,46897.0,118.0,0,corporate auto,suv,322.575677
+yx35990,california,m,master,5479.555081,56005.0,68.0,1,corporate auto,four-door car,326.4
+rn61682,california,f,bachelor,2114.738469,0.0,72.0,5,personal auto,four-door car,518.4
+bb10681,california,f,college,4140.648654,0.0,123.0,2,corporate auto,suv,590.4
+fh43628,california,f,college,25464.82059,13663.0,97.0,0,personal auto,four-door car,465.6
+wz45103,california,f,bachelor,5678.050167,0.0,76.0,0,personal auto,four-door car,364.8
+cb59349,california,m,master,16261.5855,60646.0,134.0,0,personal auto,suv,643.2
+rx91025,california,m,high school or below,19872.262,0.0,185.0,0,personal auto,suv,1950.725547
+ac13887,california,m,bachelor,4628.995325,0.0,67.0,0,corporate auto,two-door car,482.4
+tf56202,california,m,college,5032.165498,66367.0,64.0,0,personal auto,two-door car,307.2
+ym19146,california,f,college,4100.398533,47761.0,104.0,0,personal auto,four-door car,541.282007
+la72316,california,m,bachelor,23405.98798,71941.0,73.0,0,personal auto,four-door car,198.234764
+pk87824,california,f,college,3096.511217,21604.0,79.0,0,corporate auto,four-door car,379.2
+td14365,california,m,bachelor,8163.890428,0.0,85.0,3,corporate auto,four-door car,790.784983
+up19263,california,m,college,7524.442436,21941.0,96.0,0,personal auto,four-door car,691.2
+y167826,california,m,college,2611.836866,0.0,77.0,0,corporate auto,two-door car,369.6
diff --git a/.ipynb_checkpoints/lab-dw-data-structuring-and-combining-checkpoint.ipynb b/.ipynb_checkpoints/lab-dw-data-structuring-and-combining-checkpoint.ipynb
new file mode 100644
index 0000000..d89a340
--- /dev/null
+++ b/.ipynb_checkpoints/lab-dw-data-structuring-and-combining-checkpoint.ipynb
@@ -0,0 +1,1190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "25d7736c-ba17-4aff-b6bb-66eba20fbf4e",
+   "metadata": {
+    "id": "25d7736c-ba17-4aff-b6bb-66eba20fbf4e"
+   },
+   "source": [
+    "# Lab | Data Structuring and Combining Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2cdfc70-44c8-478c-81e7-2bc43fdf4986",
+   "metadata": {
+    "id": "a2cdfc70-44c8-478c-81e7-2bc43fdf4986"
+   },
+   "source": [
+    "## Challenge 1: Combining & Cleaning Data\n",
+    "\n",
+    "In this challenge, we will be working with the customer data from an insurance company, as we did in the two previous labs. The data can be found here:\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\n",
+    "\n",
+    "But this time, we got new data, which can be found in the following 2 CSV files located at the links below.\n",
+    "\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\n",
+    "\n",
+    "Note that you'll need to clean and format the new data.\n",
+    "\n",
+    "Observation:\n",
+    "- One option is to first combine the three datasets and then apply the cleaning function to the new combined dataset\n",
+    "- Another option would be to read the clean file you saved in the previous lab, and just clean the two new files and concatenate the three clean datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "492d06e3-92c7-4105-ac72-536db98d3244",
+   "metadata": {
+    "id": "492d06e3-92c7-4105-ac72-536db98d3244"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5fdbc4b4-cb41-41fe-8c52-8ac0dda08240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file1 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\")\n",
+    "file2 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\")\n",
+    "file3 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\")\n",
+    "\n",
+    "for f in (file1, file2, file3):\n",
+    "    f.rename(columns={\"State\": \"ST\", \"Gender\": \"GENDER\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "18dc22da-d8f4-4e0c-a156-a2a412e235fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Customer</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>GENDER</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2.704934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953.59%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131.464935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743.17%</td>\n",
+       "      <td>48767.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566.472247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>764586.18%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529.881344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GA49547</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>536307.65%</td>\n",
+       "      <td>36357.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17.269323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer          ST GENDER             Education Customer Lifetime Value  \\\n",
+       "0  RB50392  Washington    NaN                Master                     NaN   \n",
+       "1  QZ44356     Arizona      F              Bachelor              697953.59%   \n",
+       "2  AI49188      Nevada      F              Bachelor             1288743.17%   \n",
+       "3  WW63253  California      M              Bachelor              764586.18%   \n",
+       "4  GA49547  Washington      M  High School or Below              536307.65%   \n",
+       "\n",
+       "    Income  Monthly Premium Auto Number of Open Complaints     Policy Type  \\\n",
+       "0      0.0                1000.0                    1/0/00   Personal Auto   \n",
+       "1      0.0                  94.0                    1/0/00   Personal Auto   \n",
+       "2  48767.0                 108.0                    1/0/00   Personal Auto   \n",
+       "3      0.0                 106.0                    1/0/00  Corporate Auto   \n",
+       "4  36357.0                  68.0                    1/0/00   Personal Auto   \n",
+       "\n",
+       "   Vehicle Class  Total Claim Amount  \n",
+       "0  Four-Door Car            2.704934  \n",
+       "1  Four-Door Car         1131.464935  \n",
+       "2   Two-Door Car          566.472247  \n",
+       "3            SUV          529.881344  \n",
+       "4  Four-Door Car           17.269323  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([file1, file2, file3], ignore_index=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a31e4938-293c-4088-a7b2-2b841688a471",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Customer', 'ST', 'GENDER', 'Education', 'Customer Lifetime Value',\n",
+       "       'Income', 'Monthly Premium Auto', 'Number of Open Complaints',\n",
+       "       'Policy Type', 'Vehicle Class', 'Total Claim Amount'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "08612b71-87d6-47d2-85e4-73eb49764595",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#uniformizar\n",
+    "df.columns = df.columns.str.lower().str.replace(\" \", \"_\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ecf11dd0-e6f6-490e-9d51-59f4a1140b08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.rename(columns={\n",
+    "    \"customer\": \"customer_id\",\n",
+    "    \"st\": \"state\",\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "dcc8b6ee-7c76-4c6c-a226-f35a3aab3db9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#removing columns \n",
+    "df = df.drop(columns=[\"unnamed:_0\"], errors=\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "72a4d55e-2765-41c5-9468-835e75c1a334",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer_id                  2937\n",
+       "state                        2937\n",
+       "gender                       3059\n",
+       "education                    2937\n",
+       "customer_lifetime_value      2944\n",
+       "income                       2937\n",
+       "monthly_premium_auto         2937\n",
+       "number_of_open_complaints    2937\n",
+       "policy_type                  2937\n",
+       "vehicle_class                2937\n",
+       "total_claim_amount           2937\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#to show missing values\n",
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "91310a83-ca30-4cc6-b507-cf9231643147",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#for numerical variables : fill Nan with 0 \n",
+    "df = df.fillna({col: 0 for col in df.select_dtypes(include=\"number\").columns})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "58e58b49-1631-415a-a8be-d07f99832a8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#for categorical variables : filling null text with unknown\n",
+    "df = df.fillna({col: \"Unknown\" for col in df.select_dtypes(include=\"object\").columns})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "6be35cc1-8c70-4572-9864-7f826713c2bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#correcting data types\n",
+    "df[\"customer_id\"] = df[\"customer_id\"].astype(str)\n",
+    "df[\"income\"] = df[\"income\"].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "1bb8ae8c-3825-4ca5-91fb-dff0c44ec54a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# identifiying column with text\n",
+    "cat_cols = df.select_dtypes(include=\"object\").columns\n",
+    "\n",
+    "# cleaning text\n",
+    "for col in cat_cols:\n",
+    "    df[col] = df[col].astype(str).str.strip().str.lower()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "79144d21-2732-4c38-abb3-c3e86bdac173",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# standardize gender values\n",
+    "df[\"gender\"] = df[\"gender\"].replace({\n",
+    "    \"femal\": \"f\",\n",
+    "    \"female\": \"f\",\n",
+    "    \"male\": \"m\",\n",
+    "})\n",
+    "\n",
+    "# standardize state values\n",
+    "df[\"state\"] = df[\"state\"].replace({\n",
+    "    \"cali\": \"california\",\n",
+    "    \"az\": \"arizona\",\n",
+    "    \"wa\": \"washington\",\n",
+    "})\n",
+    "\n",
+    "# standardize education spelling\n",
+    "df[\"education\"] = df[\"education\"].replace({\"bachelors\": \"bachelor\"})\n",
+    "\n",
+    "# group luxury vehicle classes together\n",
+    "df[\"vehicle_class\"] = df[\"vehicle_class\"].replace({\n",
+    "    \"sports car\": \"luxury\",\n",
+    "    \"luxury suv\": \"luxury\",\n",
+    "    \"luxury car\": \"luxury\",\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "2d49c970-a3d4-42b2-9c2c-faa285e991a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# customer_lifetime_value comes in as a percentage string\n",
+    "df[\"customer_lifetime_value\"] = df[\"customer_lifetime_value\"].astype(str).str.replace(\"%\", \"\", regex=False)\n",
+    "df[\"customer_lifetime_value\"] = pd.to_numeric(df[\"customer_lifetime_value\"], errors=\"coerce\").fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "5f7ccf25-c7fa-4c94-aa8c-1542365550e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# number_of_open_complaints\n",
+    "# number is the actual complaint count\n",
+    "def clean_complaints(x):\n",
+    "    x = str(x)\n",
+    "    if \"/\" in x:\n",
+    "        return int(x.split(\"/\")[1])\n",
+    "    try:\n",
+    "        return int(float(x))\n",
+    "    except ValueError:\n",
+    "        return 0\n",
+    "\n",
+    "df[\"number_of_open_complaints\"] = df[\"number_of_open_complaints\"].apply(clean_complaints)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "8304012b-c4d7-44bd-84e8-c93b6581be52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9135, 11)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop exact duplicate rows produced by combining the 3 files\n",
+    "df = df.drop_duplicates().reset_index(drop=True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "103a5eb5-06a9-447a-b41d-e6b20069e35b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer_id                  0\n",
+       "state                        0\n",
+       "gender                       0\n",
+       "education                    0\n",
+       "customer_lifetime_value      0\n",
+       "income                       0\n",
+       "monthly_premium_auto         0\n",
+       "number_of_open_complaints    0\n",
+       "policy_type                  0\n",
+       "vehicle_class                0\n",
+       "total_claim_amount           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "271230a1-570d-40ac-9ddd-5b2ed7a1b7cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer_id</th>\n",
+       "      <th>state</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rb50392</td>\n",
+       "      <td>washington</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>master</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>four-door car</td>\n",
+       "      <td>2.704934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>qz44356</td>\n",
+       "      <td>arizona</td>\n",
+       "      <td>f</td>\n",
+       "      <td>bachelor</td>\n",
+       "      <td>697953.59</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>four-door car</td>\n",
+       "      <td>1131.464935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ai49188</td>\n",
+       "      <td>nevada</td>\n",
+       "      <td>f</td>\n",
+       "      <td>bachelor</td>\n",
+       "      <td>1288743.17</td>\n",
+       "      <td>48767.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>two-door car</td>\n",
+       "      <td>566.472247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ww63253</td>\n",
+       "      <td>california</td>\n",
+       "      <td>m</td>\n",
+       "      <td>bachelor</td>\n",
+       "      <td>764586.18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>corporate auto</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>529.881344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ga49547</td>\n",
+       "      <td>washington</td>\n",
+       "      <td>m</td>\n",
+       "      <td>high school or below</td>\n",
+       "      <td>536307.65</td>\n",
+       "      <td>36357.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>four-door car</td>\n",
+       "      <td>17.269323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer_id       state   gender             education  \\\n",
+       "0     rb50392  washington  unknown                master   \n",
+       "1     qz44356     arizona        f              bachelor   \n",
+       "2     ai49188      nevada        f              bachelor   \n",
+       "3     ww63253  california        m              bachelor   \n",
+       "4     ga49547  washington        m  high school or below   \n",
+       "\n",
+       "   customer_lifetime_value   income  monthly_premium_auto  \\\n",
+       "0                     0.00      0.0                1000.0   \n",
+       "1                697953.59      0.0                  94.0   \n",
+       "2               1288743.17  48767.0                 108.0   \n",
+       "3                764586.18      0.0                 106.0   \n",
+       "4                536307.65  36357.0                  68.0   \n",
+       "\n",
+       "   number_of_open_complaints     policy_type  vehicle_class  \\\n",
+       "0                          0   personal auto  four-door car   \n",
+       "1                          0   personal auto  four-door car   \n",
+       "2                          0   personal auto   two-door car   \n",
+       "3                          0  corporate auto            suv   \n",
+       "4                          0   personal auto  four-door car   \n",
+       "\n",
+       "   total_claim_amount  \n",
+       "0            2.704934  \n",
+       "1         1131.464935  \n",
+       "2          566.472247  \n",
+       "3          529.881344  \n",
+       "4           17.269323  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "952dcd18-2e4d-4833-bc99-6049935ad925",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# saving he cleaned & combined dataset\n",
+    "df.to_csv(\"clean_combined_customer_data.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31b8a9e7-7db9-4604-991b-ef6771603e57",
+   "metadata": {
+    "id": "31b8a9e7-7db9-4604-991b-ef6771603e57"
+   },
+   "source": [
+    "# Challenge 2: Structuring Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a877fd6d-7a0c-46d2-9657-f25036e4ca4b",
+   "metadata": {
+    "id": "a877fd6d-7a0c-46d2-9657-f25036e4ca4b"
+   },
+   "source": [
+    "In this challenge, we will continue to work with customer data from an insurance company, but we will use a dataset with more columns, called marketing_customer_analysis.csv, which can be found at the following link:\n",
+    "\n",
+    "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\n",
+    "\n",
+    "This dataset contains information such as customer demographics, policy details, vehicle information, and the customer's response to the last marketing campaign. Our goal is to explore and analyze this data by performing data cleaning, formatting, and structuring."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
+   "metadata": {
+    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10910, 27)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "marketing = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\")\n",
+    "marketing.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "a9d5251e-3817-466d-96dc-835a977d28dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unnamed:_0</th>\n",
+       "      <th>customer</th>\n",
+       "      <th>state</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>response</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>education</th>\n",
+       "      <th>effective_to_date</th>\n",
+       "      <th>employmentstatus</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>policy</th>\n",
+       "      <th>renew_offer_type</th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>vehicle_size</th>\n",
+       "      <th>vehicle_type</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>DK49336</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>4809.216960</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-02-18</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>292.800000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>KX64629</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2228.525238</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-18</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>744.924331</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>LZ68649</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>14947.917300</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-02-10</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>480.000000</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>QA50777</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>9025.067525</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-01-17</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>707.925645</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unnamed:_0 customer       state  customer_lifetime_value response  \\\n",
+       "0           0  DK49336     Arizona              4809.216960       No   \n",
+       "1           1  KX64629  California              2228.525238       No   \n",
+       "2           2  LZ68649  Washington             14947.917300       No   \n",
+       "3           3  XL78013      Oregon             22332.439460      Yes   \n",
+       "4           4  QA50777      Oregon              9025.067525       No   \n",
+       "\n",
+       "   coverage education effective_to_date employmentstatus gender  ...  \\\n",
+       "0     Basic   College        2011-02-18         Employed      M  ...   \n",
+       "1     Basic   College        2011-01-18       Unemployed      F  ...   \n",
+       "2     Basic  Bachelor        2011-02-10         Employed      M  ...   \n",
+       "3  Extended   College        2011-01-11         Employed      M  ...   \n",
+       "4   Premium  Bachelor        2011-01-17    Medical Leave      F  ...   \n",
+       "\n",
+       "   number_of_policies     policy_type        policy  renew_offer_type  \\\n",
+       "0                   9  Corporate Auto  Corporate L3            Offer3   \n",
+       "1                   1   Personal Auto   Personal L3            Offer4   \n",
+       "2                   2   Personal Auto   Personal L3            Offer3   \n",
+       "3                   2  Corporate Auto  Corporate L3            Offer2   \n",
+       "4                   7   Personal Auto   Personal L2            Offer1   \n",
+       "\n",
+       "   sales_channel  total_claim_amount  vehicle_class  vehicle_size  \\\n",
+       "0          Agent          292.800000  Four-Door Car       Medsize   \n",
+       "1    Call Center          744.924331  Four-Door Car       Medsize   \n",
+       "2    Call Center          480.000000            SUV       Medsize   \n",
+       "3         Branch          484.013411  Four-Door Car       Medsize   \n",
+       "4         Branch          707.925645  Four-Door Car       Medsize   \n",
+       "\n",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "2            A     2  \n",
+       "3            A     1  \n",
+       "4            A     1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "marketing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "4a7baedf-1d0e-439a-bfe3-c62568281ca6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unnamed:_0                       0\n",
+       "customer                         0\n",
+       "state                            0\n",
+       "customer_lifetime_value          0\n",
+       "response                         0\n",
+       "coverage                         0\n",
+       "education                        0\n",
+       "effective_to_date                0\n",
+       "employmentstatus                 0\n",
+       "gender                           0\n",
+       "income                           0\n",
+       "location_code                    0\n",
+       "marital_status                   0\n",
+       "monthly_premium_auto             0\n",
+       "months_since_last_claim          0\n",
+       "months_since_policy_inception    0\n",
+       "number_of_open_complaints        0\n",
+       "number_of_policies               0\n",
+       "policy_type                      0\n",
+       "policy                           0\n",
+       "renew_offer_type                 0\n",
+       "sales_channel                    0\n",
+       "total_claim_amount               0\n",
+       "vehicle_class                    0\n",
+       "vehicle_size                     0\n",
+       "vehicle_type                     0\n",
+       "month                            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "marketing.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df35fd0d-513e-4e77-867e-429da10a9cc7",
+   "metadata": {
+    "id": "df35fd0d-513e-4e77-867e-429da10a9cc7"
+   },
+   "source": [
+    "1. You work at the marketing department and you want to know which sales channel brought the most sales in terms of total revenue. Using pivot, create a summary table showing the total revenue for each sales channel (branch, call center, web, and mail).\n",
+    "Round the total revenue to 2 decimal points.  Analyze the resulting table to draw insights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "ac1502ba-8f72-496f-b360-2801c97a47ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agent</th>\n",
+       "      <td>1810226.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Branch</th>\n",
+       "      <td>1301204.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Call Center</th>\n",
+       "      <td>926600.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web</th>\n",
+       "      <td>706600.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               total_claim_amount\n",
+       "sales_channel                    \n",
+       "Agent                  1810226.82\n",
+       "Branch                 1301204.00\n",
+       "Call Center             926600.82\n",
+       "Web                     706600.04"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "revenue_by_channel = marketing.pivot_table(\n",
+    "    index=\"sales_channel\",\n",
+    "    values=\"total_claim_amount\",\n",
+    "    aggfunc=\"sum\"\n",
+    ").round(2)\n",
+    "\n",
+    "revenue_by_channel = revenue_by_channel.sort_values(\"total_claim_amount\", ascending=False)\n",
+    "revenue_by_channel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bc34a84-ed7b-4d18-bb78-18399d0708ec",
+   "metadata": {},
+   "source": [
+    "#Insight\n",
+    "Agents had the most revenue (~1.81M), followed by branch, call center and web (only 0.70M), which shows that Web is underperforming relatively to the others sales channel and this could be interesting to investigate the \"why\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "640993b2-a291-436c-a34d-a551144f8196",
+   "metadata": {
+    "id": "640993b2-a291-436c-a34d-a551144f8196"
+   },
+   "source": [
+    "2. Create a pivot table that shows the average customer lifetime value per gender and education level. Analyze the resulting table to draw insights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2270c145-31fb-4198-8dfb-9e3b54f53f20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>education</th>\n",
+       "      <th>Bachelor</th>\n",
+       "      <th>College</th>\n",
+       "      <th>Doctor</th>\n",
+       "      <th>High School or Below</th>\n",
+       "      <th>Master</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>7874.27</td>\n",
+       "      <td>7748.82</td>\n",
+       "      <td>7328.51</td>\n",
+       "      <td>8675.22</td>\n",
+       "      <td>8157.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7703.60</td>\n",
+       "      <td>8052.46</td>\n",
+       "      <td>7415.33</td>\n",
+       "      <td>8149.69</td>\n",
+       "      <td>8168.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "education  Bachelor  College   Doctor  High School or Below   Master\n",
+       "gender                                                              \n",
+       "F           7874.27  7748.82  7328.51               8675.22  8157.05\n",
+       "M           7703.60  8052.46  7415.33               8149.69  8168.83"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clv_by_gender_education = marketing.pivot_table(\n",
+    "    index=\"gender\",\n",
+    "    columns=\"education\",\n",
+    "    values=\"customer_lifetime_value\",\n",
+    "    aggfunc=\"mean\"\n",
+    ").round(2)\n",
+    "\n",
+    "clv_by_gender_education"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30e2006b-e204-49bf-92ae-22754a66b8f7",
+   "metadata": {},
+   "source": [
+    "#Insight:\n",
+    "the average customer lifetime value shows no big difference between genders and across education levels.\n",
+    "we can say that based on that, the education level does not drive customer value. "
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:anaconda3]",
+   "language": "python",
+   "name": "conda-env-anaconda3-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/clean_combined_customer_data.csv b/clean_combined_customer_data.csv
new file mode 100644
index 0000000..4f95dd5
--- /dev/null
+++ b/clean_combined_customer_data.csv
@@ -0,0 +1,9136 @@
+customer_id,state,gender,education,customer_lifetime_value,income,monthly_premium_auto,number_of_open_complaints,policy_type,vehicle_class,total_claim_amount
+rb50392,washington,unknown,master,0.0,0.0,1000.0,0,personal auto,four-door car,2.704934
+qz44356,arizona,f,bachelor,697953.59,0.0,94.0,0,personal auto,four-door car,1131.464935
+ai49188,nevada,f,bachelor,1288743.17,48767.0,108.0,0,personal auto,two-door car,566.472247
+ww63253,california,m,bachelor,764586.18,0.0,106.0,0,corporate auto,suv,529.881344
+ga49547,washington,m,high school or below,536307.65,36357.0,68.0,0,personal auto,four-door car,17.269323
+oc83172,oregon,f,bachelor,825629.78,62902.0,69.0,0,personal auto,two-door car,159.383042
+xz87318,oregon,f,college,538089.86,55350.0,67.0,0,corporate auto,four-door car,321.6
+cf85061,arizona,m,master,721610.03,0.0,101.0,0,corporate auto,four-door car,363.02968
+dy87989,oregon,m,bachelor,2412750.4,14072.0,71.0,0,corporate auto,four-door car,511.2
+bq94931,oregon,f,college,738817.81,28812.0,93.0,0,special auto,four-door car,425.527834
+sx51350,california,m,college,473899.2,0.0,67.0,0,personal auto,four-door car,482.4
+vq65197,california,unknown,college,819719.71,0.0,110.0,0,personal auto,suv,528.0
+dp39365,california,unknown,master,879879.7,77026.0,110.0,2,corporate auto,four-door car,472.029737
+sj95423,arizona,unknown,high school or below,881901.89,99845.0,110.0,1,corporate auto,suv,528.0
+il66569,california,unknown,college,538443.17,83689.0,70.0,2,corporate auto,four-door car,307.139132
+bw63560,oregon,unknown,bachelor,746313.94,24599.0,64.0,1,corporate auto,four-door car,42.920271
+fv94802,nevada,unknown,high school or below,256686.78,25049.0,67.0,0,personal auto,two-door car,454.245098
+oe15005,california,unknown,college,394524.16,28855.0,101.0,0,personal auto,suv,647.442031
+wc83389,oregon,unknown,college,571033.31,51148.0,72.0,0,personal auto,four-door car,308.981664
+fl50705,california,unknown,high school or below,816261.71,66140.0,101.0,0,corporate auto,four-door car,484.8
+zk25313,oregon,unknown,high school or below,287205.13,57749.0,74.0,0,personal auto,two-door car,355.2
+qk46697,washington,m,bachelor,617710.93,61040.0,79.0,1,personal auto,two-door car,20.382876
+yh23384,arizona,unknown,bachelor,2412750.4,14072.0,71.0,0,personal auto,four-door car,511.2
+tz98966,nevada,unknown,bachelor,245019.1,0.0,73.0,3,corporate auto,four-door car,554.376763
+hm55802,california,unknown,bachelor,239210.79,17870.0,61.0,0,corporate auto,four-door car,439.2
+fs42516,oregon,unknown,college,580206.6,97541.0,72.0,0,personal auto,four-door car,389.185006
+us89481,california,unknown,bachelor,394637.21,0.0,111.0,0,personal auto,four-door car,799.2
+hs14476,washington,m,college,916206.32,29723.0,80.0,0,personal auto,four-door car,20.985105
+ge62437,arizona,unknown,college,1290256.01,86584.0,111.0,2,personal auto,four-door car,532.8
+ej77678,oregon,unknown,master,323536.05,75690.0,80.0,1,personal auto,four-door car,384.0
+sv85652,arizona,unknown,college,245458.35,23158.0,63.0,1,personal auto,four-door car,322.294043
+ul64533,nevada,unknown,high school or below,1897545.61,65999.0,237.0,0,corporate auto,luxury,615.927769
+pf41800,california,unknown,bachelor,471532.13,0.0,65.0,0,personal auto,four-door car,308.15089
+hd95276,washington,f,college,473787.17,0.0,130.0,0,personal auto,suv,23.820158
+sk67821,oregon,unknown,bachelor,493291.63,37260.0,62.0,0,corporate auto,four-door car,15.437681
+yv55495,arizona,unknown,high school or below,574422.97,68987.0,71.0,0,personal auto,four-door car,204.475147
+ky38074,california,unknown,bachelor,1389173.57,42305.0,117.0,0,personal auto,four-door car,561.6
+dm79012,oregon,unknown,master,738097.67,65706.0,91.0,0,personal auto,four-door car,436.8
+cm61827,oregon,unknown,bachelor,309003.41,0.0,90.0,0,personal auto,two-door car,648.0
+wc35801,arizona,unknown,high school or below,252163.31,53243.0,66.0,2,personal auto,four-door car,157.397849
+qg25316,nevada,unknown,high school or below,265206.18,0.0,70.0,1,corporate auto,two-door car,484.318536
+mb98372,oregon,unknown,college,277104.5,50071.0,71.0,0,corporate auto,two-door car,18.918935
+il19217,california,unknown,bachelor,393900.64,60021.0,99.0,0,personal auto,four-door car,882.871945
+sr38658,arizona,unknown,high school or below,1223187.97,43244.0,103.0,0,personal auto,luxury,494.4
+yd87931,washington,m,doctor,495165.61,46896.0,35354.0,1,personal auto,four-door car,31.707317
+hg65722,oregon,unknown,doctor,1281910.29,10105.0,172.0,3,personal auto,suv,0.517753
+bu27331,arizona,unknown,bachelor,446851.05,0.0,73.0,3,personal auto,four-door car,579.165954
+xm45289,oregon,unknown,high school or below,551434.4,23218.0,71.0,0,personal auto,two-door car,447.79344
+kp34198,california,unknown,bachelor,334387.53,0.0,92.0,0,corporate auto,four-door car,529.624084
+sh90947,arizona,unknown,high school or below,229447.89,0.0,62.0,0,personal auto,four-door car,313.023175
+we95729,oregon,unknown,college,3670742.64,24804.0,104.0,0,personal auto,suv,593.830288
+py51963,california,unknown,bachelor,3347334.95,33190.0,106.0,0,corporate auto,suv,508.8
+rb69909,nevada,unknown,high school or below,798343.17,36014.0,69.0,3,special auto,four-door car,173.956072
+nw21079,washington,f,master,487938.48,67163.0,61.0,2,personal auto,two-door car,33.192803
+fr46645,california,unknown,bachelor,429399.73,16701.0,113.0,0,personal auto,four-door car,831.625979
+sy17488,arizona,unknown,college,716439.55,46623.0,91.0,0,corporate auto,four-door car,436.8
+ap67935,california,unknown,college,761951.58,64749.0,64.0,0,special auto,four-door car,302.56519
+fs37417,arizona,unknown,high school or below,395800.28,0.0,101.0,0,personal auto,suv,484.8
+ml29312,oregon,unknown,high school or below,449949.33,16969.0,124.0,2,personal auto,suv,704.768111
+ub61619,oregon,unknown,master,405956.74,11621.0,108.0,0,personal auto,four-door car,518.4
+cd86811,arizona,unknown,bachelor,445811.34,17622.0,65.0,1,personal auto,four-door car,312.0
+ru83859,california,unknown,bachelor,811033.31,11489.0,105.0,0,personal auto,two-door car,504.0
+fg63582,oregon,unknown,bachelor,333976.49,0.0,94.0,0,personal auto,two-door car,863.327324
+nn71951,california,unknown,high school or below,2426101.78,66525.0,100.0,0,personal auto,suv,104.331355
+wb37082,arizona,unknown,bachelor,661397.37,0.0,63.0,0,personal auto,four-door car,676.391482
+sm52139,washington,unknown,college,293069.35,33663.0,73.0,0,personal auto,four-door car,350.4
+fl82372,oregon,unknown,college,867219.43,22547.0,112.0,0,corporate auto,suv,537.6
+dp45816,arizona,unknown,high school or below,1163866.93,61486.0,97.0,0,personal auto,two-door car,465.258644
+gw33762,oregon,unknown,bachelor,684615.03,0.0,95.0,0,personal auto,two-door car,456.0
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+ra89822,california,f,high school or below,34611.37896,20090.0,109.0,0,personal auto,luxury,523.2
+ij25462,california,m,bachelor,2845.520933,86631.0,73.0,1,personal auto,four-door car,400.832857
+zp86347,california,f,college,5500.577411,44019.0,138.0,0,personal auto,suv,165.478147
+ho82823,california,f,bachelor,3358.532935,59367.0,84.0,0,personal auto,four-door car,6.880385
+ea54906,california,f,high school or below,7501.661322,38874.0,94.0,0,corporate auto,four-door car,331.170924
+bc64697,california,f,college,11663.98572,0.0,107.0,0,personal auto,four-door car,513.6
+vv68726,california,f,high school or below,5133.397765,28647.0,69.0,0,personal auto,four-door car,331.2
+qr91631,california,f,high school or below,8732.090534,51205.0,72.0,0,personal auto,two-door car,185.530076
+ot56964,california,m,master,9424.256842,46897.0,118.0,0,corporate auto,suv,322.575677
+yx35990,california,m,master,5479.555081,56005.0,68.0,1,corporate auto,four-door car,326.4
+rn61682,california,f,bachelor,2114.738469,0.0,72.0,5,personal auto,four-door car,518.4
+bb10681,california,f,college,4140.648654,0.0,123.0,2,corporate auto,suv,590.4
+fh43628,california,f,college,25464.82059,13663.0,97.0,0,personal auto,four-door car,465.6
+wz45103,california,f,bachelor,5678.050167,0.0,76.0,0,personal auto,four-door car,364.8
+cb59349,california,m,master,16261.5855,60646.0,134.0,0,personal auto,suv,643.2
+rx91025,california,m,high school or below,19872.262,0.0,185.0,0,personal auto,suv,1950.725547
+ac13887,california,m,bachelor,4628.995325,0.0,67.0,0,corporate auto,two-door car,482.4
+tf56202,california,m,college,5032.165498,66367.0,64.0,0,personal auto,two-door car,307.2
+ym19146,california,f,college,4100.398533,47761.0,104.0,0,personal auto,four-door car,541.282007
+la72316,california,m,bachelor,23405.98798,71941.0,73.0,0,personal auto,four-door car,198.234764
+pk87824,california,f,college,3096.511217,21604.0,79.0,0,corporate auto,four-door car,379.2
+td14365,california,m,bachelor,8163.890428,0.0,85.0,3,corporate auto,four-door car,790.784983
+up19263,california,m,college,7524.442436,21941.0,96.0,0,personal auto,four-door car,691.2
+y167826,california,m,college,2611.836866,0.0,77.0,0,corporate auto,two-door car,369.6
diff --git a/lab-dw-data-structuring-and-combining.ipynb b/lab-dw-data-structuring-and-combining.ipynb
index ec4e3f9..d89a340 100644
--- a/lab-dw-data-structuring-and-combining.ipynb
+++ b/lab-dw-data-structuring-and-combining.ipynb
@@ -36,14 +36,604 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "492d06e3-92c7-4105-ac72-536db98d3244",
    "metadata": {
     "id": "492d06e3-92c7-4105-ac72-536db98d3244"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5fdbc4b4-cb41-41fe-8c52-8ac0dda08240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file1 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\")\n",
+    "file2 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\")\n",
+    "file3 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\")\n",
+    "\n",
+    "for f in (file1, file2, file3):\n",
+    "    f.rename(columns={\"State\": \"ST\", \"Gender\": \"GENDER\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "18dc22da-d8f4-4e0c-a156-a2a412e235fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Customer</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>GENDER</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2.704934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953.59%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131.464935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743.17%</td>\n",
+       "      <td>48767.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566.472247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>764586.18%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529.881344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GA49547</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>536307.65%</td>\n",
+       "      <td>36357.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17.269323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer          ST GENDER             Education Customer Lifetime Value  \\\n",
+       "0  RB50392  Washington    NaN                Master                     NaN   \n",
+       "1  QZ44356     Arizona      F              Bachelor              697953.59%   \n",
+       "2  AI49188      Nevada      F              Bachelor             1288743.17%   \n",
+       "3  WW63253  California      M              Bachelor              764586.18%   \n",
+       "4  GA49547  Washington      M  High School or Below              536307.65%   \n",
+       "\n",
+       "    Income  Monthly Premium Auto Number of Open Complaints     Policy Type  \\\n",
+       "0      0.0                1000.0                    1/0/00   Personal Auto   \n",
+       "1      0.0                  94.0                    1/0/00   Personal Auto   \n",
+       "2  48767.0                 108.0                    1/0/00   Personal Auto   \n",
+       "3      0.0                 106.0                    1/0/00  Corporate Auto   \n",
+       "4  36357.0                  68.0                    1/0/00   Personal Auto   \n",
+       "\n",
+       "   Vehicle Class  Total Claim Amount  \n",
+       "0  Four-Door Car            2.704934  \n",
+       "1  Four-Door Car         1131.464935  \n",
+       "2   Two-Door Car          566.472247  \n",
+       "3            SUV          529.881344  \n",
+       "4  Four-Door Car           17.269323  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([file1, file2, file3], ignore_index=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a31e4938-293c-4088-a7b2-2b841688a471",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Customer', 'ST', 'GENDER', 'Education', 'Customer Lifetime Value',\n",
+       "       'Income', 'Monthly Premium Auto', 'Number of Open Complaints',\n",
+       "       'Policy Type', 'Vehicle Class', 'Total Claim Amount'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "08612b71-87d6-47d2-85e4-73eb49764595",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#uniformizar\n",
+    "df.columns = df.columns.str.lower().str.replace(\" \", \"_\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ecf11dd0-e6f6-490e-9d51-59f4a1140b08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.rename(columns={\n",
+    "    \"customer\": \"customer_id\",\n",
+    "    \"st\": \"state\",\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "dcc8b6ee-7c76-4c6c-a226-f35a3aab3db9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#removing columns \n",
+    "df = df.drop(columns=[\"unnamed:_0\"], errors=\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "72a4d55e-2765-41c5-9468-835e75c1a334",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer_id                  2937\n",
+       "state                        2937\n",
+       "gender                       3059\n",
+       "education                    2937\n",
+       "customer_lifetime_value      2944\n",
+       "income                       2937\n",
+       "monthly_premium_auto         2937\n",
+       "number_of_open_complaints    2937\n",
+       "policy_type                  2937\n",
+       "vehicle_class                2937\n",
+       "total_claim_amount           2937\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#to show missing values\n",
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "91310a83-ca30-4cc6-b507-cf9231643147",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#for numerical variables : fill Nan with 0 \n",
+    "df = df.fillna({col: 0 for col in df.select_dtypes(include=\"number\").columns})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "58e58b49-1631-415a-a8be-d07f99832a8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#for categorical variables : filling null text with unknown\n",
+    "df = df.fillna({col: \"Unknown\" for col in df.select_dtypes(include=\"object\").columns})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "6be35cc1-8c70-4572-9864-7f826713c2bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#correcting data types\n",
+    "df[\"customer_id\"] = df[\"customer_id\"].astype(str)\n",
+    "df[\"income\"] = df[\"income\"].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "1bb8ae8c-3825-4ca5-91fb-dff0c44ec54a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# identifiying column with text\n",
+    "cat_cols = df.select_dtypes(include=\"object\").columns\n",
+    "\n",
+    "# cleaning text\n",
+    "for col in cat_cols:\n",
+    "    df[col] = df[col].astype(str).str.strip().str.lower()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "79144d21-2732-4c38-abb3-c3e86bdac173",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# standardize gender values\n",
+    "df[\"gender\"] = df[\"gender\"].replace({\n",
+    "    \"femal\": \"f\",\n",
+    "    \"female\": \"f\",\n",
+    "    \"male\": \"m\",\n",
+    "})\n",
+    "\n",
+    "# standardize state values\n",
+    "df[\"state\"] = df[\"state\"].replace({\n",
+    "    \"cali\": \"california\",\n",
+    "    \"az\": \"arizona\",\n",
+    "    \"wa\": \"washington\",\n",
+    "})\n",
+    "\n",
+    "# standardize education spelling\n",
+    "df[\"education\"] = df[\"education\"].replace({\"bachelors\": \"bachelor\"})\n",
+    "\n",
+    "# group luxury vehicle classes together\n",
+    "df[\"vehicle_class\"] = df[\"vehicle_class\"].replace({\n",
+    "    \"sports car\": \"luxury\",\n",
+    "    \"luxury suv\": \"luxury\",\n",
+    "    \"luxury car\": \"luxury\",\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "2d49c970-a3d4-42b2-9c2c-faa285e991a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# customer_lifetime_value comes in as a percentage string\n",
+    "df[\"customer_lifetime_value\"] = df[\"customer_lifetime_value\"].astype(str).str.replace(\"%\", \"\", regex=False)\n",
+    "df[\"customer_lifetime_value\"] = pd.to_numeric(df[\"customer_lifetime_value\"], errors=\"coerce\").fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "5f7ccf25-c7fa-4c94-aa8c-1542365550e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# number_of_open_complaints\n",
+    "# number is the actual complaint count\n",
+    "def clean_complaints(x):\n",
+    "    x = str(x)\n",
+    "    if \"/\" in x:\n",
+    "        return int(x.split(\"/\")[1])\n",
+    "    try:\n",
+    "        return int(float(x))\n",
+    "    except ValueError:\n",
+    "        return 0\n",
+    "\n",
+    "df[\"number_of_open_complaints\"] = df[\"number_of_open_complaints\"].apply(clean_complaints)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "8304012b-c4d7-44bd-84e8-c93b6581be52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9135, 11)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop exact duplicate rows produced by combining the 3 files\n",
+    "df = df.drop_duplicates().reset_index(drop=True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "103a5eb5-06a9-447a-b41d-e6b20069e35b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customer_id                  0\n",
+       "state                        0\n",
+       "gender                       0\n",
+       "education                    0\n",
+       "customer_lifetime_value      0\n",
+       "income                       0\n",
+       "monthly_premium_auto         0\n",
+       "number_of_open_complaints    0\n",
+       "policy_type                  0\n",
+       "vehicle_class                0\n",
+       "total_claim_amount           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "271230a1-570d-40ac-9ddd-5b2ed7a1b7cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer_id</th>\n",
+       "      <th>state</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rb50392</td>\n",
+       "      <td>washington</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>master</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>four-door car</td>\n",
+       "      <td>2.704934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>qz44356</td>\n",
+       "      <td>arizona</td>\n",
+       "      <td>f</td>\n",
+       "      <td>bachelor</td>\n",
+       "      <td>697953.59</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>four-door car</td>\n",
+       "      <td>1131.464935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ai49188</td>\n",
+       "      <td>nevada</td>\n",
+       "      <td>f</td>\n",
+       "      <td>bachelor</td>\n",
+       "      <td>1288743.17</td>\n",
+       "      <td>48767.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>two-door car</td>\n",
+       "      <td>566.472247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ww63253</td>\n",
+       "      <td>california</td>\n",
+       "      <td>m</td>\n",
+       "      <td>bachelor</td>\n",
+       "      <td>764586.18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>corporate auto</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>529.881344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ga49547</td>\n",
+       "      <td>washington</td>\n",
+       "      <td>m</td>\n",
+       "      <td>high school or below</td>\n",
+       "      <td>536307.65</td>\n",
+       "      <td>36357.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>personal auto</td>\n",
+       "      <td>four-door car</td>\n",
+       "      <td>17.269323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer_id       state   gender             education  \\\n",
+       "0     rb50392  washington  unknown                master   \n",
+       "1     qz44356     arizona        f              bachelor   \n",
+       "2     ai49188      nevada        f              bachelor   \n",
+       "3     ww63253  california        m              bachelor   \n",
+       "4     ga49547  washington        m  high school or below   \n",
+       "\n",
+       "   customer_lifetime_value   income  monthly_premium_auto  \\\n",
+       "0                     0.00      0.0                1000.0   \n",
+       "1                697953.59      0.0                  94.0   \n",
+       "2               1288743.17  48767.0                 108.0   \n",
+       "3                764586.18      0.0                 106.0   \n",
+       "4                536307.65  36357.0                  68.0   \n",
+       "\n",
+       "   number_of_open_complaints     policy_type  vehicle_class  \\\n",
+       "0                          0   personal auto  four-door car   \n",
+       "1                          0   personal auto  four-door car   \n",
+       "2                          0   personal auto   two-door car   \n",
+       "3                          0  corporate auto            suv   \n",
+       "4                          0   personal auto  four-door car   \n",
+       "\n",
+       "   total_claim_amount  \n",
+       "0            2.704934  \n",
+       "1         1131.464935  \n",
+       "2          566.472247  \n",
+       "3          529.881344  \n",
+       "4           17.269323  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "952dcd18-2e4d-4833-bc99-6049935ad925",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# saving he cleaned & combined dataset\n",
+    "df.to_csv(\"clean_combined_customer_data.csv\", index=False)"
    ]
   },
   {
@@ -72,14 +662,298 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
    "metadata": {
     "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10910, 27)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "marketing = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\")\n",
+    "marketing.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "a9d5251e-3817-466d-96dc-835a977d28dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unnamed:_0</th>\n",
+       "      <th>customer</th>\n",
+       "      <th>state</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>response</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>education</th>\n",
+       "      <th>effective_to_date</th>\n",
+       "      <th>employmentstatus</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>policy</th>\n",
+       "      <th>renew_offer_type</th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>vehicle_size</th>\n",
+       "      <th>vehicle_type</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>DK49336</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>4809.216960</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-02-18</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>292.800000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>KX64629</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2228.525238</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-18</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>744.924331</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>LZ68649</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>14947.917300</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-02-10</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>480.000000</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>QA50777</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>9025.067525</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-01-17</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>707.925645</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unnamed:_0 customer       state  customer_lifetime_value response  \\\n",
+       "0           0  DK49336     Arizona              4809.216960       No   \n",
+       "1           1  KX64629  California              2228.525238       No   \n",
+       "2           2  LZ68649  Washington             14947.917300       No   \n",
+       "3           3  XL78013      Oregon             22332.439460      Yes   \n",
+       "4           4  QA50777      Oregon              9025.067525       No   \n",
+       "\n",
+       "   coverage education effective_to_date employmentstatus gender  ...  \\\n",
+       "0     Basic   College        2011-02-18         Employed      M  ...   \n",
+       "1     Basic   College        2011-01-18       Unemployed      F  ...   \n",
+       "2     Basic  Bachelor        2011-02-10         Employed      M  ...   \n",
+       "3  Extended   College        2011-01-11         Employed      M  ...   \n",
+       "4   Premium  Bachelor        2011-01-17    Medical Leave      F  ...   \n",
+       "\n",
+       "   number_of_policies     policy_type        policy  renew_offer_type  \\\n",
+       "0                   9  Corporate Auto  Corporate L3            Offer3   \n",
+       "1                   1   Personal Auto   Personal L3            Offer4   \n",
+       "2                   2   Personal Auto   Personal L3            Offer3   \n",
+       "3                   2  Corporate Auto  Corporate L3            Offer2   \n",
+       "4                   7   Personal Auto   Personal L2            Offer1   \n",
+       "\n",
+       "   sales_channel  total_claim_amount  vehicle_class  vehicle_size  \\\n",
+       "0          Agent          292.800000  Four-Door Car       Medsize   \n",
+       "1    Call Center          744.924331  Four-Door Car       Medsize   \n",
+       "2    Call Center          480.000000            SUV       Medsize   \n",
+       "3         Branch          484.013411  Four-Door Car       Medsize   \n",
+       "4         Branch          707.925645  Four-Door Car       Medsize   \n",
+       "\n",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "2            A     2  \n",
+       "3            A     1  \n",
+       "4            A     1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "marketing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "4a7baedf-1d0e-439a-bfe3-c62568281ca6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unnamed:_0                       0\n",
+       "customer                         0\n",
+       "state                            0\n",
+       "customer_lifetime_value          0\n",
+       "response                         0\n",
+       "coverage                         0\n",
+       "education                        0\n",
+       "effective_to_date                0\n",
+       "employmentstatus                 0\n",
+       "gender                           0\n",
+       "income                           0\n",
+       "location_code                    0\n",
+       "marital_status                   0\n",
+       "monthly_premium_auto             0\n",
+       "months_since_last_claim          0\n",
+       "months_since_policy_inception    0\n",
+       "number_of_open_complaints        0\n",
+       "number_of_policies               0\n",
+       "policy_type                      0\n",
+       "policy                           0\n",
+       "renew_offer_type                 0\n",
+       "sales_channel                    0\n",
+       "total_claim_amount               0\n",
+       "vehicle_class                    0\n",
+       "vehicle_size                     0\n",
+       "vehicle_type                     0\n",
+       "month                            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code goes here"
+    "marketing.isna().sum()"
    ]
   },
   {
@@ -94,50 +968,198 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "640993b2-a291-436c-a34d-a551144f8196",
-   "metadata": {
-    "id": "640993b2-a291-436c-a34d-a551144f8196"
-   },
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "ac1502ba-8f72-496f-b360-2801c97a47ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agent</th>\n",
+       "      <td>1810226.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Branch</th>\n",
+       "      <td>1301204.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Call Center</th>\n",
+       "      <td>926600.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web</th>\n",
+       "      <td>706600.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               total_claim_amount\n",
+       "sales_channel                    \n",
+       "Agent                  1810226.82\n",
+       "Branch                 1301204.00\n",
+       "Call Center             926600.82\n",
+       "Web                     706600.04"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "2. Create a pivot table that shows the average customer lifetime value per gender and education level. Analyze the resulting table to draw insights."
+    "revenue_by_channel = marketing.pivot_table(\n",
+    "    index=\"sales_channel\",\n",
+    "    values=\"total_claim_amount\",\n",
+    "    aggfunc=\"sum\"\n",
+    ").round(2)\n",
+    "\n",
+    "revenue_by_channel = revenue_by_channel.sort_values(\"total_claim_amount\", ascending=False)\n",
+    "revenue_by_channel"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "32c7f2e5-3d90-43e5-be33-9781b6069198",
-   "metadata": {
-    "id": "32c7f2e5-3d90-43e5-be33-9781b6069198"
-   },
+   "id": "2bc34a84-ed7b-4d18-bb78-18399d0708ec",
+   "metadata": {},
    "source": [
-    "## Bonus\n",
-    "\n",
-    "You work at the customer service department and you want to know which months had the highest number of complaints by policy type category. Create a summary table showing the number of complaints by policy type and month.\n",
-    "Show it in a long format table."
+    "#Insight\n",
+    "Agents had the most revenue (~1.81M), followed by branch, call center and web (only 0.70M), which shows that Web is underperforming relatively to the others sales channel and this could be interesting to investigate the \"why\"."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e3d09a8f-953c-448a-a5f8-2e5a8cca7291",
+   "id": "640993b2-a291-436c-a34d-a551144f8196",
    "metadata": {
-    "id": "e3d09a8f-953c-448a-a5f8-2e5a8cca7291"
+    "id": "640993b2-a291-436c-a34d-a551144f8196"
    },
    "source": [
-    "*In data analysis, a long format table is a way of structuring data in which each observation or measurement is stored in a separate row of the table. The key characteristic of a long format table is that each column represents a single variable, and each row represents a single observation of that variable.*\n",
-    "\n",
-    "*More information about long and wide format tables here: https://www.statology.org/long-vs-wide-data/*"
+    "2. Create a pivot table that shows the average customer lifetime value per gender and education level. Analyze the resulting table to draw insights."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "3a069e0b-b400-470e-904d-d17582191be4",
-   "metadata": {
-    "id": "3a069e0b-b400-470e-904d-d17582191be4"
-   },
-   "outputs": [],
+   "execution_count": 50,
+   "id": "2270c145-31fb-4198-8dfb-9e3b54f53f20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>education</th>\n",
+       "      <th>Bachelor</th>\n",
+       "      <th>College</th>\n",
+       "      <th>Doctor</th>\n",
+       "      <th>High School or Below</th>\n",
+       "      <th>Master</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>7874.27</td>\n",
+       "      <td>7748.82</td>\n",
+       "      <td>7328.51</td>\n",
+       "      <td>8675.22</td>\n",
+       "      <td>8157.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7703.60</td>\n",
+       "      <td>8052.46</td>\n",
+       "      <td>7415.33</td>\n",
+       "      <td>8149.69</td>\n",
+       "      <td>8168.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "education  Bachelor  College   Doctor  High School or Below   Master\n",
+       "gender                                                              \n",
+       "F           7874.27  7748.82  7328.51               8675.22  8157.05\n",
+       "M           7703.60  8052.46  7415.33               8149.69  8168.83"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clv_by_gender_education = marketing.pivot_table(\n",
+    "    index=\"gender\",\n",
+    "    columns=\"education\",\n",
+    "    values=\"customer_lifetime_value\",\n",
+    "    aggfunc=\"mean\"\n",
+    ").round(2)\n",
+    "\n",
+    "clv_by_gender_education"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30e2006b-e204-49bf-92ae-22754a66b8f7",
+   "metadata": {},
    "source": [
-    "# Your code goes here"
+    "#Insight:\n",
+    "the average customer lifetime value shows no big difference between genders and across education levels.\n",
+    "we can say that based on that, the education level does not drive customer value. "
    ]
   }
  ],
@@ -146,9 +1168,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:anaconda3]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-anaconda3-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -160,7 +1182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,